Latest / iwon™ podcast with Michael J Thomas / He Invested $4M to Save a Million Lives | Dr Stephen Charlap | iwon™ podcast | Season 2, Episode 2

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He Invested $4M to Save a Million Lives | Dr Stephen Charlap | iwon™ podcast | Season 2, Episode 2 · iwon™ podcast with Michael J Thomas

• 0:00
  I don't want anybody else to go through what I went through with my brother, period.
• 0:03
  I put in over 4 million of my own money.
• 0:05
  I work seven days a week.
• 0:07
  Can I actually become better at diagnosis than your doctor?
• 0:11
  Should you trust ChatGPT with your health?
• 0:14
  Not one doctor has ever said to me, I'm not going to use your pride because I'm smarter than it.
• 0:20
  How do you build credibility when you're creating something new?
• 0:23
  How do you know when to keep going or when to change direction?
• 0:26
  What happens when you care so much about someone solving the problem that you risk getting it all wrong?
• 0:31
  Failure is easy, but success is possible.
• 0:33
  I like that.
• 0:34
  Failure is easy, but success is possible.
• 0:37
  Welcome to the i1 podcast.
• 0:39
  If you're building something difficult, something people don't immediately understand, or something you genuinely believe needs to exist in the world, this episode's for you.
• 0:49
  Because today we're going to explore what happens when purpose, money, technology, and reality collide.
• 0:54
  Dr.
• 0:54
  Stephen Charlap is a doctor, an entrepreneur who's already built and exited a hugely successful healthcare company serving millions of patients.
• 1:04
  He could have stopped there.
• 1:05
  After losing his brother, he went back into the fire and committed himself to solving a problem he believes costs hundreds of thousands of people their lives.
• 1:15
  Along the way, he's invested millions of his own money, wrestled with the commercial model, encountered ageism from investors, and built at the intersection of medicine and artificial intelligence.
• 1:26
  We're going to talk about all of that, but we're also going to get into some much bigger questions.
• 1:31
  Most business podcasts are bs, so I built one that isn't.
• 1:34
  Real founders, real stories, like Stephen's.
• 1:36
  The mistakes, the difficult bits, and what it actually takes to build something meaningful.
• 1:40
  Dr.
• 1:41
  Stephen, Charlotte, welcome to the podcast.
• 1:44
  Thanks for having me,.
• 1:45
  Steven.
• 1:45
  You've already achieved something.
• 1:47
  Most entrepreneurs spent their whole careers chasing you, built health drives, served millions of patients, and exited.
• 1:55
  Why weren't you done?
• 1:56
  And did the exit feel as good as you expected it would?
• 2:00
  My wife asked me that question every day because my sacrifice has been her sacrifice.
• 2:06
  Unfortunately, it was born out of tragedy.
• 2:10
  My older brother, a practicing cardiologist, was misdiagnosed.
• 2:14
  He was a brother I shared a bedroom with growing up.
• 2:17
  He.
• 2:17
  He was a brother who always tended to me, took care of me, bought me birthday presents, let me put my head on his lap on hot, sweaty car rides while we sat in the back of the Rambler.
• 2:29
  He was the brother that told me that I could get an A in every class that I took in college.
• 2:36
  Inspired me, mentored me, and though I got into many medical schools.
• 2:41
  I went to the same one as him because I wanted to follow in his footsteps.
• 2:45
  New York University School of Medicine, the Grossman School.
• 2:47
  His unfortunate demise stirred something very passionate in me.
• 2:53
  And sometimes, obviously, I always wish he hadn't passed away, but sometimes I feel I wasn't affected as badly as I was and haven't taken this on because it's been a Herculean journey.
• 3:07
  Who were you before that happened, and who were you afterwards?
• 3:15
  My first business, I was a poor kid with two degrees, an MD and an mba.
• 3:21
  And I wanted to do well by doing good.
• 3:25
  But we're really talking about putting food on the table for my kids at that time.
• 3:30
  And I had three kids for my two kids at that time, and solving a problem that I identified.
• 3:37
  Before I started menom, I opened up a Primary Prevention Medical Center.
• 3:42
  It was during that tenure that what happened to my brother happened.
• 3:46
  And I was already a changed person because I had made my money and I was really looking to give back to society.
• 3:53
  And I opened up a medical center whose focus was to reduce your reliance on doctors by helping you with mental health issues, helping you with physical activities issues, helping you with nutritional issues, helping you with sleep issues, basically
• 4:13
  addressing the issues that lead to illness and require you to spend so much time going to doctors and paying doctors for problems that could have potentially been preventable or detected early.
• 4:27
  So the treatments are much more effective and much shorter and much less costly.
• 4:32
  So I already had that experience.
• 4:34
  I was really changed already by that point.
• 4:36
  But then my brother's loss hit me particularly hard because while I was running the Prevention Medicine Center, I was giving lectures all over the place.
• 4:46
  And I became a very popular lecturer.
• 4:48
  And what was I lecturing on?
• 4:51
  How most of what happens to you is under your control.
• 4:55
  And then my brother died from something that.
• 4:57
  That was not under his control.
• 4:58
  And that gave me pause for thought.
• 5:01
  Am I really telling everybody?
• 5:03
  Have I bought into the belief that wellness trumps illness?
• 5:11
  And I ended up getting accepted to a program at Stanford, ended up staying there three and a half years, and then two years at Harvard studying misdiagnosis and diagnosis, but also genetics.
• 5:26
  And that completely transformed my perspective, because much of what happens to us is because of genetics, but we trigger it.
• 5:36
  So genetics is like the gun.
• 5:38
  We pull the trigger with lifestyle medications or social situations, so on and so forth.
• 5:46
  So my perspective changed dramatically from when I opened up the Preventive Medicine center to when I left Stanford and Harvard and went off and started mental.
• 5:58
  Were the clues already there?
• 6:00
  Do you feel when it came to your brother passing away early, was the failure a lack of knowledge or failure for doctors to join the dots?
• 6:10
  The.
• 6:11
  Clues were family history.
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  Doctors don't take a detailed family history wide.
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  They takes more time.
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  Most doctors also aren't trained in the details of family history because they figure that's something a geneticist or genetic counsel can deal with.
• 6:26
  The problem is if you don't know the history, then you don't know the patient needs a geneticist or genetic counselor.
• 6:33
  I know that you talked to me about how medicine often asks the wrong question, like, what is this?
• 6:43
  Your approach is very much, what else could this be?
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  And that question could save lives.
• 6:48
  Yeah, well, again, that's a whole discussion about the technology we built, why it's superior to any other technology of the sort.
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  We have a patent issued on our approach, and in fact, recently we've tested it on 18 hematology cases.
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  Hematology is blood disorders, and it's one of the hardest specialties in medicine.
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  And we were given these initially three cases, and we were told one of the cases is virtually impossible to diagnose.
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  And we nailed all three, and then we were given 15 more cases, and we nailed them as well.
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  Now we've been given a final 10, and when we finish the final 10, we're going to publish the 28 results.
• 7:28
  Because nobody is reporting perfection in diagnosis.
• 7:34
  I'm not telling you we can perfectly get every single diagnosis because sometimes you need whole genome sequencing, sometimes you need autopsy.
• 7:43
  Okay, obviously we can't get an autopsy or a very deep biopsy, so.
• 7:49
  But if it's possible to get a diagnosis, there is no better application to get the diagnosis.
• 7:54
  But I think what you're really talking about is how I feel about physicians.
• 7:58
  I've evolved as a physician into believing that whatever physician tells you, you need to validate it or verify it some way.
• 8:09
  What I call trust but verify, not my phrase back to the Reagan days, trust but verify.
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  Because they're human and they're under a lot of pressure and they don't give you enough time and they don't get paid for spending more time with you, finding out more about you, they will do the best they can
• 8:30
  in the 12 to 15 to 20 minutes that they allocate per patient.
• 8:34
  And if they can't figure it out in that their attitude is come back.
• 8:38
  Are you saying that AI could ultimately become better at diagnosis than your doctor then why would I go to a doctor first?
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  What's the.
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  What's the danger?
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  Could AI consider so many possibilities that it creates false alarms as well.
• 8:54
  Is that a concern in what you're building to try and avoid that?
• 8:57
  I'm not concerned about that because the way we built our model.
• 9:01
  But the reality is only doctors can prescribe medications.
• 9:04
  So if you need medication, if you need surgery, only a doctor can provide that.
• 9:09
  But imagine if you come better prepared to your doctor's appointment.
• 9:13
  Two studies showed that if you come in with an analysis of your history that shows the possible diagnoses that you might have, the doctor is 8 to 12% more accurate in their own diagnosis.
• 9:28
  If you give them that at the end of the appointment, no benefit, only if you give it to them before they take your history.
• 9:37
  And yet that's what doctors do, they diagnose.
• 9:41
  So it's very hard as a human, where you see your identity in taking a history and making the diagnosis, for somebody to come along and say there's a technology that's already better at you at most of the cognitive
• 9:56
  functions within health care.
• 9:59
  And it's unbelievable the amount of fear mongering being perpetuated by physicians who believe that they are under threat.
• 10:10
  The problem is AI is not threatening them.
• 10:13
  There's a shortage of physicians, particularly primary care physicians.
• 10:18
  It's reported that over 100 million Americans already don't have access, 4.2 billion people on the planet don't have access.
• 10:26
  And physician numbers are shrinking around primary care.
• 10:30
  One, those going to concierge medicine to make more money and see fewer patients.
• 10:35
  Those going to direct primary care who don't accept insurance.
• 10:40
  So if you're an average American with insurance who can't afford to pay a small ransom or treasure to a doctor to be one of his 300 to 600 patients, you're smack out of luck.
• 10:59
  And that's most of the population.
• 11:01
  I don't have the statistics in front of me, but it's a very, very high percentage of people who go into bankruptcy and have to sell their home as a result of even cancer treatment, because again, often it's not
• 11:11
  covered or not insured.
• 11:12
  That's another conversation.
• 11:14
  Do you believe, with doctors being such skeptics, do you think it can actually create credibility and work hand in hand?
• 11:22
  Is it qualifications, evidence, patents, other doctors believing you, real outcomes?
• 11:28
  Or is it something else?
• 11:31
  Look, everybody wants you to treat AI like a medication, where you do these extensive studies and you prove beyond a shadow of a doubt or with minimal risks that it works incredibly well.
• 11:49
  The problem is the AI models are evolving so quickly that if you did that study, it would be meaningless one way or the other, because a new model in a new model in a new model has already come
• 12:03
  out.
• 12:04
  And by the way, this is not a hypothetical situation.
• 12:08
  I'm involved in a study right now in the United Kingdom that cost over $3 million, whose mission is to identify ways to earlier detect lung in pancreatic cancer.
• 12:24
  And they're using a two year old version of my technology, which to me is like a toy gun, like I would say a water gun to a semi automatic machine gun.
• 12:41
  Okay.
• 12:42
  That's the difference between what they're using and what we now have in our possession.
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  But you can't insert what we now have in our possession because the study has to stick with what they started studying two years,.
• 12:56
  It has to be consistent with what they had.
• 12:58
  Correct.
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  So I'm witnessing firsthand the dangers of trying to constrain and treat AI with this rigors of validation that physicians trying to protect themselves are demanding in order to embrace it.
• 13:21
  If you're in a situation where you've got a two year old version of your own platform and they're evaluating based on that, which is almost sounds, it sounds unfair just to be very vanilla about that, that does sound
• 13:33
  very unfair.
• 13:35
  How do you know when to persevere and when to change direction with health services, health advocates who are so AI resistant,.
• 13:43
  You know, actually they're probably afraid.
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  I wouldn't even want to survive.
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  The old product because it's a waste of time and effort and energy.
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  We could do literally a 50 times better job with the new technology than the old technology from two years ago.
• 14:03
  That's how much has evolved within two years.
• 14:07
  Look, people are afraid that AI is a runaway train, but the reality is it may eventually bring us terminator type destruction.
• 14:18
  Who can speak to that?
• 14:19
  Okay, I can't.
• 14:22
  But what I do know, it can diagnose better than human doctors already today.
• 14:29
  That's what I know, that's what I want to see happen.
• 14:34
  I don't want anybody else to go through what I went through with my brother.
• 14:37
  Do you believe your software, your algorithms, could have saved your brother's life?
• 14:42
  Is that a fair statement to make?
• 14:43
  Do I believe I am 100% certain that my brother would still be here if that application existed when he was alive?
• 14:57
  Here's the reality.
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  There are certain diseases, even if you catch them early, people die from it.
• 15:05
  But if you catch cancer very early, you can intervene and maintain strict surveillance over it and extend life by decades, preventing it by preemptive measures, improving early detection, by surveillance, and by getting the right diagnosis the first Time,
• 15:33
  so you treat it as effectively as possible.
• 15:38
  Now, the last one is the least hopeful because once a cancer has progressed to a certain point, sometimes there's no coming back.
• 15:49
  But the first two, preventing by preemptive measures.
• 15:53
  Like if you know you have a significant high risk for breast cancer and you remove your breasts.
• 16:01
  Because of genetic,.
• 16:02
  Because of genetics, because family history.
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  Okay, so not just family history, not just genetics, but genetics plus family history,.
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  Plus enough data to convince a doctor that this is something.
• 16:17
  Worth know the risk of disease is incredibly high.
• 16:21
  Just like in the case of Angelina Jolie, she had family history and she had a positive mutation and her probability of breast cancer was 80%.
• 16:31
  Okay.
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  Because it was the appropriate thing to do.
• 16:35
  And again, I think it's important pointing out one thing that you touched on earlier.
• 16:40
  Right.
• 16:41
  A lot of people don't have access to the sort of things that Angelina Jolie, as a famous wealthy person, had access to.
• 16:49
  Presumably your sort of drive is to enable anyone to be able to access that level of understanding about themselves and for doctors, so that less people die unnecessarily.
• 17:04
  Yeah.
• 17:05
  So if I may ask, because this is very much a podcast for founders, as you've been going through all these models, Right.
• 17:13
  How did you identify what to let go of and what to keep, what ideas to focus on, what to let go of, because you're a very experienced guy.
• 17:21
  So.
• 17:21
  So I'm sure you've got some advice on that.
• 17:23
  Well, I ignored good advice, my own, because I mentor startups and I've mentored hundreds of startups, invested in 30 plus startups.
• 17:34
  And I always tell them, solve a simple problem, a clear problem, a simple, clearly defined, easy to describe in one sentence, A problem that people are definitely bothered by.
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  A problem that people are willing to pay to solve.
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  A problem that they will pay you enough to solve, that it is worth your effort to solve it.
• 18:02
  A problem that no one else is offering them, a viable alternative.
• 18:07
  And by viable you mean an actually good, useful thing to help them solve that problem.
• 18:13
  Because a lot of the advice I come across, like when I speak to founders, just because somebody's in a market doesn't mean the opportunity is gone.
• 18:20
  If you can do it different and better, that's also good,.
• 18:23
  Correct?
• 18:24
  Look, Clayton Christensen of Harvard Business School, who I met when I was at Harvard Business School and at one of my reunions, he spoke as well.
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  He wrote a book called Competing with Luck.
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  And I've always said to people, better to be lucky than smart.
• 18:41
  But this book, the title appealed to me on a Great level.
• 18:45
  Clayton Christensen, by the way, who's unfortunately now deceased, is also the person who coined the phrase and wrote the books about disruptive innovation.
• 18:54
  And in this book, Competing with Luck, which I strongly recommend your audience go get a copy of right now.
• 19:00
  Great book.
• 19:01
  He says that people buy from you because they have a job to be done.
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  And that job has three components.
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  A functional component, a social component, and an emotional component.
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  If you check off those three boxes, the functional, the social and emotional reason for them to buy your product because it will do those three jobs for them.
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  That is a formula for success.
• 19:30
  And he gives explicit examples of that.
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  So what you're saying, just so I fully understand, is functional.
• 19:37
  It does what it needs to do social.
• 19:39
  That's the bit that I.
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  The emotional part.
• 19:41
  I get it.
• 19:42
  I feel better now that the problem solved.
• 19:44
  But the social part,.
• 19:45
  Well, let's think about an electric screwdriver, okay?
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  And why you buy it?
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  Because you want to screw in something.
• 19:52
  And the reason you want to screw in something, because you want to hang something on that.
• 19:56
  And you're going to hang something on that.
• 19:57
  It's going to make you feel good.
• 19:58
  And it's also going to make your house look nice.
• 20:01
  And on a social level, that's going to encourage socialization of some sort.
• 20:07
  That's not a great example, but I think it points to the social aspects of why we do something.
• 20:15
  Okay.
• 20:16
  Human beings do something for emotional reasons, for functional reasons, and for social reasons.
• 20:22
  Oh, so you're referring to somebody.
• 20:26
  I don't recommend this, by the way.
• 20:28
  Buying an expensive sports car to show that they're doing really well.
• 20:33
  That serves a social status element.
• 20:36
  Right?
• 20:37
  Emotion,.
• 20:38
  Knowing you've arrived because you're driving this expensive car.
• 20:41
  And functionally because you needed a car.
• 20:44
  And it also goes faster.
• 20:46
  What would you recommend then?
• 20:48
  To a founder who's building something genuinely new, like genuinely new, and keeps hearing from investors, from feedback?
• 20:56
  I don't get it.
• 20:57
  What should they do?
• 20:58
  There's an old acronym, KISS K I S Says keep it simple.
• 21:01
  Stupid.
• 21:02
  Founders always think that they have to tell investors about all the great things they're going to do next after they succeed with this product.
• 21:10
  Let me tell you something.
• 21:11
  None of the great companies thought that way, okay?
• 21:14
  Nike created running shoes.
• 21:16
  He didn't have any idea he was going to become this Sports Emporium.
• 21:20
  Okay?
• 21:21
  McDonald's was lunch.
• 21:23
  How do you know that they were going to become dinner and breakfast?
• 21:27
  So many companies that are the biggest staples of our society started with one simple thing, and they excelled at it some didn't even excel at the first thing, like Twitter, okay?
• 21:41
  They started doing something else.
• 21:42
  But they did one thing and they did it well.
• 21:47
  So leave the dreaming about all the other things you're going to do.
• 21:50
  You know how many people say, our product does this, this, this and that, and.
• 21:53
  And I say, oh, wow, can I try it?
• 21:55
  No, nothing's built yet.
• 21:57
  I said, so why are you talking about it's going to do this, this, this and that?
• 22:01
  Tell me the one thing it's going to do.
• 22:02
  Why it's important.
• 22:03
  Who's it going to do it for?
• 22:05
  Who's going to pay for it?
• 22:06
  Okay, Are they going to pay you enough?
• 22:09
  Is it worth your time?
• 22:11
  Is it going to solve a problem that is already clearly identified?
• 22:15
  This is the problem.
• 22:16
  How do you take that advice?
• 22:19
  It's cost me a lot of money and a lot of time.
• 22:21
  I'm on a mission to eliminate misdiagnosis.
• 22:23
  I believe I have, by and large, eliminated misdiagnosis by building a technology that no one's built before patenting it, testing on thousands, getting money from Harvard, Stanford, the nih, the nhs, continuing it to test it in every possible
• 22:41
  venue that I can.
• 22:43
  But guess what?
• 22:45
  Nobody cares.
• 22:46
  The universities don't want to hear about it.
• 22:48
  Because you want me to test your product.
• 22:51
  Pay me.
• 22:52
  The big tech companies probably violating some of my patents already, they want to invent it on their own, okay?
• 23:00
  They don't even want to test us against them.
• 23:02
  I've invited every big tech company to test our product against their leading product in the diagnosis space.
• 23:08
  They don't want to find out.
• 23:09
  It's lose lose from their perspective, right?
• 23:12
  If they.
• 23:12
  Walking.
• 23:13
  If you give the usurper a shot at the title before they put in the work, you run the risk that you lose the title.
• 23:21
  Do you think that people trust doctors and medicine less than they did 20 years ago?
• 23:29
  If so, why?
• 23:30
  And what has medicine itself done to contribute?
• 23:34
  And lastly, if I may, what do people unfairly judge doctors by?
• 23:39
  You know, like,.
• 23:40
  Listen, a doctor is expected within their community to live a certain standard.
• 23:45
  To live a certain standard, you have to make a certain amount of money, okay?
• 23:49
  To make a certain amount of money with the way the system treats doctors, which is incredibly poorly.
• 23:55
  Okay?
• 23:56
  You got to hustle.
• 23:58
  And by definition, when you hustle, you make more mistakes, you spend less time with each problem.
• 24:05
  You become a jack of all trades, master of none.
• 24:08
  And so the Institute of Medicine, the US Institute of Medicine, now predicts everyone's going to be misdiagnosed at Some point in their lives.
• 24:16
  And you mentioned at the beginning of this podcast, nearly 400,000 people a year die from misdiagnosis.
• 24:24
  Another 400,000 plus become permanently disabled.
• 24:29
  That works out to every 42 seconds somebody dies or is permanently disabled.
• 24:35
  How the hell can you trust human beings who have that track record to be the final word on your body, your health, your well being?
• 24:48
  I say you can't.
• 24:51
  Okay, There's a big debate right now.
• 24:54
  Can AI replace them?
• 24:55
  Right now, I believe with the right setup, with a human in the loop, but not a doctor.
• 25:02
  A human in the loop trained on the AI to make sure that the AI doesn't function inappropriately.
• 25:09
  Why AI store a computer?
• 25:11
  Computer's great.
• 25:13
  Computers don't always perfectly function.
• 25:16
  Technology doesn't always perfectly function.
• 25:19
  We need somebody to know when the technology is failing.
• 25:23
  Not from a healthcare perspective, because the AI knows so much more about medicine than doctors do already, but from a technology perspective that it's failing.
• 25:31
  You know, taking AI out of the equation for a second, do you agree that people trust doctors, medicine, science less than they did 20 years ago?
• 25:39
  And if so, why?
• 25:40
  Is it because of that misdiagnosis, or do you think There are many factors.
• 25:44
  A lot of doctors operating under burnout conditions, okay, they have their own depression, they have their own irritability because they're under these time constraints every year.
• 25:57
  Their reimbursement doesn't keep up with office costs, and they're struggling to maintain the same amount of compensation, particularly in an inflationary environment.
• 26:10
  And they are not the same people that they were tens and twenties and thirties and forty years ago.
• 26:18
  Listen, when I was in my surgical residency, the chief surgeon got up at what's called the grand rounds, which is a Friday meeting once a month where all the residents are and all the attendings are in the auditorium.
• 26:31
  And he said, the Harvard MBAs are ruining medicine.
• 26:35
  They know nothing about medicine and they're ruining medicine.
• 26:38
  I had just been accepted to Harvard, and so I walked out saying, yeah, he doesn't, you know, he's just jealous, which is what I think it was.
• 26:46
  But he's so demoralized my fellow residents that it was a sight to be seen.
• 26:54
  They walked out of that room with their heads down to the person muttering to themselves, I should have gone to business school.
• 27:01
  I should have gone to law school.
• 27:03
  I heard people muttering to themselves, that's the end of medicine.
• 27:07
  It was something called DRGs.
• 27:09
  And that's what ruined medicine.
• 27:11
  Because pre1982 DRGs, diagnostic related groups, doctors were Kings.
• 27:20
  They were on the golf course by 3 o' clock in the afternoon, virtually guaranteed, okay?
• 27:27
  Because they made their money and they had time to spare.
• 27:31
  So they were able to operate at optimal efficiency because they weren't burnt out and enjoy golf.
• 27:36
  If I may ask, when do you think healthy skepticism with medicine can become dangerous?
• 27:41
  And has science become too politicized?
• 27:44
  Listen, nobody has, excuse my French to balls to tackle these powers to be, these powerful insurance companies, these powerful pharmaceutical companies, these very large health systems, these very large electronic medical record vendors.
• 27:59
  These are really powerful people.
• 28:01
  I mean, billions of dollars involved.
• 28:04
  How do you beat them?
• 28:06
  Disruption.
• 28:08
  I believe in disruption.
• 28:10
  Is it going to work?
• 28:11
  I don't know.
• 28:12
  Oftentimes Goliath wins.
• 28:13
  You know the reason we always talk about the story of David and Goliath?
• 28:16
  Because it's really unusual, okay, that the little punk beat this massive master of war, because that was Goliath.
• 28:28
  And that, to be fair, in the startup world, is.
• 28:30
  Is the big companies just acquiring and shelving anybody it sees as a competitor.
• 28:36
  Yeah.
• 28:37
  Do I expect somebody to want me to acquire me?
• 28:39
  Yes.
• 28:40
  But I really hope that the financial pressures don't get to me early where I don't get a chance to save a million lives or more.
• 28:50
  In memory of my brother, okay?
• 28:54
  That's why I do this.
• 28:55
  I don't take any compensation.
• 28:57
  I put in over 4 million of my own money.
• 29:00
  I work seven days a week.
• 29:02
  I am fully committed to solving this problem once and for all.
• 29:07
  Some describe it as boiling the ocean.
• 29:09
  That's the mistake I made.
• 29:11
  I built every piece that was necessary to solve diagnosis.
• 29:17
  It's a multifactorial process.
• 29:20
  Each piece is a major contributor.
• 29:23
  It's like a car.
• 29:25
  The car doesn't run unless every piece was there.
• 29:28
  So I had to build and solve multiple problems, which is why I don't recommend for people to run off on a mission like I did without fully considering the significance and gravity and the Herculean aspects of solving a
• 29:48
  problem like this.
• 29:49
  I often ask people, how do you know the difference between a Herculean task, which just requires tremendous, tremendous strength and energy, and a Sisyphean task, which is a abject illustration of failure.
• 30:04
  Sisyphean Sisyphus was a guy cursed to push a boulder up a hill, and every time he got close to the top of the hill, the boulder would roll down again.
• 30:14
  It's an exercise in futility.
• 30:16
  You've got to be a little crazy in a good way to really take on a big task.
• 30:22
  Button for punishment.
• 30:24
  A crazy and glutton for punishment.
• 30:26
  There you go.
• 30:26
  People.
• 30:27
  If you, if people are telling you you're crazy and they don't know how you keep going, you might be onto something.
• 30:34
  Yeah.
• 30:34
  And look, the investment community is highly focused on de risking.
• 30:39
  You tell them you're boiling the ocean.
• 30:40
  They're not curious, oh, how are you going to boil the ocean?
• 30:43
  Why are you going to boil the ocean?
• 30:44
  What's the benefits of boiling the ocean?
• 30:47
  What's the commercializable aspects of it?
• 30:50
  They don't care.
• 30:51
  It's just boiling the ocean.
• 30:52
  I'm not interested.
• 30:53
  And you mentioned ageism.
• 30:55
  Mention capitalism,.
• 30:56
  Which we'll tackle in a minute.
• 30:57
  Actually, I've got a really cool question for you.
• 30:59
  Do you mind if I leave that?
• 31:00
  Sure,.
• 31:01
  I'll tackle that in a minute.
• 31:02
  I just wanted to ask one thing.
• 31:03
  So just taking on the point that you mentioned about your brother and that being the mission that drove you, you've invested 4 million, you want to help impact million lives, save a million lives.
• 31:15
  Having a mission like that is incredible.
• 31:17
  Do you ever worry you might run out of road before you get to that point?
• 31:22
  Because you, you mentioned about them maybe violating the patents.
• 31:26
  It's hard to know that, obviously, but there could be.
• 31:29
  Is it something that you worry about that long before you're able to do it with all of your work that's gone?
• 31:36
  And I always say to people, go very niche for focus on a thing, focus on a problem, be specific, which is what you're doing, rather than building a general model.
• 31:44
  Yeah, look, I've been forced into focusing on men's health and men's sexual health in particular, because it is a field that is exploiting men like nothing else in society.
• 31:57
  Men are being exploited by these companies selling them pills like finasteride for their hair and testosterone for optimization and Viagra and Cialis for erectile dysfunction.
• 32:10
  Why do I say the word exploiting them and making billions?
• 32:14
  Because oftentimes these are serious medical problems.
• 32:20
  Without going into too much information, I am a victim of one of those problems.
• 32:26
  As a physician, I didn't understand the consequences.
• 32:32
  Men have no clue that Viagra, for example, because they can't sustain an erection may be masking a serious problem.
• 32:43
  And I always give this analogy because it is such a powerful analogy.
• 32:47
  In my own life, nine years old, my dad driving an old car because we were poor.
• 32:53
  Okay, Lucky he even had a car.
• 32:56
  The red light goes on in the dashboard.
• 32:59
  He says, let's go to the gas station.
• 33:01
  We go to a gas station.
• 33:02
  He comes up to one of the gas Station attendants and he says, hey, can you help me?
• 33:07
  The red light went on on my dashboard and the guy says, sure, I can disconnect the red light.
• 33:15
  True story.
• 33:16
  Nine year old never forgot it.
• 33:20
  That's what Viagra does, it disconnects the red light.
• 33:24
  It's funny how these stories stick with us, isn't it?
• 33:27
  It's basically what I think I hear you saying is that a lot of these so called cures or whatever treatments are masking something else, pretending to be the be all and end all.
• 33:38
  I myself used finasteride and minoxidil years and years ago before I finally accepted I was just losing my hair.
• 33:45
  So I completely get, you know, especially on the Internet, how these problems are being blown up to such a degree that people feel like they have no choice.
• 33:57
  They're being manipulated.
• 33:59
  Have you ever heard of post finasteride syndrome?
• 34:03
  No.
• 34:04
  Virtually no man has heard of it.
• 34:06
  It doesn't happen all the time, but it happens.
• 34:08
  It's now a syndrome where taking finasteride permanently interferes with your erections.
• 34:15
  I know that there are so many side effects to all of these things.
• 34:18
  For instance, I've heard that.
• 34:19
  Beta blockers, you got a heartburn, you're taking an H2 blocker like cimetidine interferes with erectile dysfunction.
• 34:28
  Atenolol, propranolol, SSRIs, all the major SSRIs basically interfere with erectile.
• 34:35
  Erectile function.
• 34:37
  Doctors don't tell you that.
• 34:38
  That brings a really interesting question, one that I hadn't thought about actually is also the objective, as well as helping a million people avoid early death or serious illness, is it also to, because we talked about the David
• 34:52
  Goliath thing, is it also to try and make sure that people get genuinely unbiased advice?
• 34:58
  I know it's more of a problem in the US because of the pharmaceutical companies, big pharma and healthcare companies pushing certain things.
• 35:07
  Yeah.
• 35:07
  It's also there's 28,000 diseases.
• 35:09
  The typical primary care doctor will see, give or take a thousand diseases in their career, spend 60% of their time on 25 to 50, 70% of their time on up to 250.
• 35:23
  That other thousand comes in spurts, you know, stop and go over that 30 year career, they know less than 5% of diseases.
• 35:33
  Okay.
• 35:34
  It's virtually impossible.
• 35:36
  The founder of the, the diagnosis mecca in the world 150 plus years ago said medicine already exceeds intellectual capacity, human intellectual capacity.
• 35:49
  150 years ago.
• 35:50
  We're publishing studies at a rate of about 2 million plus a year.
• 35:54
  You expected to be a jack of all trades, master of some things.
• 35:59
  Okay, they're at a significant disadvantage.
• 36:05
  AI is getting better, better and better and better.
• 36:11
  It's a no contest.
• 36:12
  Look, a study just came out trying to Fear monger.
• 36:17
  More than 70% of patients want to know if their doctor is using AI.
• 36:23
  Wrong question.
• 36:24
  The question should be why?
• 36:27
  And what difference does it make?
• 36:29
  And what AI are they using?
• 36:31
  It.
• 36:31
  Was a survey and a question meant to generate fear that doctors shouldn't use AI without telling their patients.
• 36:37
  That's not what I took away from it because I know why.
• 36:40
  They want to know if it's an AI like you said.
• 36:42
  They want to know which AI.
• 36:44
  They want to know how much of a role it played.
• 36:46
  They want to know maybe it's a positive because a doctor using AI means that they want to make sure they get the right diagnosis.
• 36:54
  So they're trying to improve the diagnostic capability by using the power of AI.
• 36:59
  Studies like that are misleading the public.
• 37:01
  And you see a beautiful marketing campaign being run against AI by systems saying AI hallucinates.
• 37:11
  Well, you know what I found?
• 37:12
  Much more than hallucination?
• 37:14
  Omissions.
• 37:15
  But you know why they don't talk about omissions?
• 37:18
  Why do they talk about hallucinations and not omissions?
• 37:22
  Because they don't admit that they don't have good quality data on particular queries, Prompts, information.
• 37:31
  That would be my guess.
• 37:32
  Okay, think about it.
• 37:33
  Doctors don't hallucinate data, but they leave out things all the time.
• 37:39
  So they're not going to complain about AI omission because they're omitters.
• 37:45
  They're going to complain about aspects of AI that they don't do.
• 37:49
  They don't hallucinate.
• 37:50
  And I suppose one of the dangers as well that I hadn't thought of until now is a human condition that we all have called confirmation bias.
• 37:57
  If we believe a particular thing, generally speaking, we will find information that confirms that for us.
• 38:04
  As opposed to being more of a detective and looking at the whole picture.
• 38:08
  Yep.
• 38:09
  That's why doctors.
• 38:10
  One of the major causes of misdiagnosis is something called premature closure.
• 38:16
  Hey, I'm pretty sure I got the right diagnosis.
• 38:19
  It's something I see all the time.
• 38:23
  If I'm wrong, you come back, we'll try again.
• 38:27
  But then people, inevitably there are like, I'm quite like, on it with my own personal health and keeping on top of things.
• 38:34
  But I know that there are a lot of people who are afraid of going to the doctor, afraid of finding out,.
• 38:38
  Particularly men that's why they get diagnosed in the emergency room with late stage cancer.
• 38:43
  That's why they, including other reasons, die six years younger than women.
• 38:47
  That's why they die at a 38% higher rate.
• 38:50
  You're taught from a young age to man up.
• 38:53
  The worst thing you could say to a man is man up because it applies to every aspect of their life.
• 38:58
  They need to be masculine, they need to be virile, they need to be strong.
• 39:03
  They don't go to the doctor just for any reason.
• 39:05
  They say in married couples, most of the medical appointments for the husband are made by the wife because the man is taught to be tough.
• 39:12
  In terms of like people using ChatGPT, right?
• 39:15
  A general AI platform that pretty much everybody knows before they go to their doctor.
• 39:22
  Is that something you think we should be worried about?
• 39:25
  Why are people doing it and what does general AI do?
• 39:30
  Well, as opposed to just looking at it as objectively as we can.
• 39:34
  You may agree or disagree with me, but I do have concerns about my most sensitive health information ending up in the wrong hands.
• 39:40
  With these AI companies.
• 39:41
  It's not private, it's admissible in court.
• 39:44
  Anything you say to an AI model could be solicited by a lawyer for a particular purpose.
• 39:49
  Second of all, it doesn't know you.
• 39:51
  No matter how much data you feed into it, it's not asking you follow up questions on your data to make sure that it truly sees the whole picture.
• 40:00
  And if you feed your electronic medical record data into that, that data is corrupt.
• 40:05
  Particularly in the United States, it's up to 50% inaccurate.
• 40:09
  And it thinks what it's reading because it sees it over and over again because doctors copied and paste.
• 40:15
  That must be the truth.
• 40:17
  I've seen those mistakes we built to protect against those mistakes.
• 40:22
  The point of the matter is, okay, you can't just say it's terrible and you can't just say it's perfect.
• 40:33
  You've got to keep working with it, keep upgrading it.
• 40:39
  Like everything and anything else.
• 40:41
  It's like a bicycle.
• 40:43
  The original motorized bicycle carts to today's cars, okay, they couldn't go very far.
• 40:52
  They needed to be cranked.
• 40:54
  They didn't have gas stations, they didn't have roads.
• 40:57
  They didn't have.
• 40:58
  My wife was mentioning the other day about it's unbelievable here in Florida that you drive these cars and the rubber doesn't melt from the heat.
• 41:07
  Well, that's because they built cars and tires with rubber that doesn't melt.
• 41:12
  The pavement is hot as anything.
• 41:15
  The sun is blaring down on the tire and it doesn't melt.
• 41:20
  Like, that's a really cool example of how we evolve and develop products for the environments in which they're needed and how we make them useful, but also last and effective and whatever you want to call it.
• 41:34
  Right.
• 41:34
  Just going back, if I may, for a second, because your analogy is really good.
• 41:38
  I'm just thinking about from the point of view of should I trust AI with health information, information that might not even tell another person, like mental health, whatever it is,.
• 41:48
  Why not?
• 41:49
  Chatgpt?
• 41:50
  Not from Claude.
• 41:51
  I'll tell you why.
• 41:52
  I use these models all the time for research that I'm constantly conducting on my own.
• 41:58
  Never do I get the full answer from any one of these models.
• 42:03
  I need to use them collectively to get the right answers.
• 42:06
  So like having a team of specialists as opposed to.
• 42:09
  Yeah,.
• 42:10
  Yeah, I never get the right answer from one of them.
• 42:13
  I need to use them collectively over and over again to get the right answers.
• 42:18
  Here's a really tangential but related question, especially when it comes to health.
• 42:27
  What are your views?
• 42:28
  Do you think, should employers or insurers get access to AI predictions about someone's health, their records?
• 42:37
  Could knowing that much about our health also have a negative impact?
• 42:41
  Could it make our lives worse in any way?
• 42:43
  Look, we have hipaa.
• 42:44
  I think HIPPA went too far and in some ways didn't go far enough.
• 42:48
  No, employers should not know about our health.
• 42:50
  When I was in business school, there was a company called Average Express.
• 42:55
  And their policy, this was before the American with Disabilities act, their policy was to discriminate against people with medical conditions.
• 43:03
  We now have a law called the American with Disabilities act that legally protects against that.
• 43:09
  But that doesn't mean that bias in people isn't still there.
• 43:13
  And if you know that somebody's going to cost you a lot in medical premiums in this day and age, which medical premiums are already really expensive?
• 43:22
  Yeah.
• 43:23
  You're not hiring somebody with medical condition.
• 43:25
  I called the CEO of Average Express morally repugnant because pre American with Disabilities act, as a physician, I found such a policy of discriminating against people based on their health conditions as morally reprehensible.
• 43:43
  We've all got different things of various natures.
• 43:47
  Right.
• 43:47
  And your point, you know, about disabilities and understanding what somebody's likely cost as an employee, not only their salary and benefits, et cetera, but also their likely time off work, sick pay, whatever.
• 44:02
  You know, obviously medical bills.
• 44:05
  Another part that kind of makes me uncomfortable and just diving a little bit deeper into this, because I'm certainly hoping that The AI companies don't take that information and try and sell it on to those companies.
• 44:15
  I hope that's not something that could happen, but I don't know.
• 44:17
  People are worried about the wrong things.
• 44:19
  What they should be worried about is can the AI give them the right answer?
• 44:25
  Can it save them a trip to the doctor?
• 44:27
  Can it catch or prevent a disease?
• 44:30
  Can it ensure they get the right diagnosis?
• 44:34
  Can they enjoy whatever amount of time they live on this earth, disease free or disease minimized?
• 44:42
  That's what people should care about.
• 44:44
  Okay?
• 44:44
  The thing about privacy is another issue.
• 44:47
  People are worried about privacy.
• 44:49
  Are you so important that your details about what illnesses you have matters to somebody?
• 44:54
  Well, to me I am.
• 44:55
  We have a word in that Amazon.
• 44:57
  It's called paranoia.
• 44:58
  A delusion of grandeur that people want to know your medical conditions because you're so important.
• 45:03
  Nothing against you personally, My medical history.
• 45:06
  Your medical history, not so important.
• 45:09
  Okay.
• 45:10
  We make it so difficult to combine quality data because of these rules.
• 45:15
  Again, they serve purposes.
• 45:17
  They also hinder good technology combinations.
• 45:22
  But look, Rome wasn't built in a day.
• 45:26
  If it was, everybody would have hired their contractors.
• 45:29
  This is not easy process.
• 45:31
  I actually think there's a really cool analogy with what you're saying.
• 45:35
  Right.
• 45:35
  I am totally on board with the idea of sharing my medical data to help everybody, like as a collective right to help each other by giving the right data so that somebody else can avoid a particular outcome or
• 45:49
  whatever it's used for.
• 45:50
  Right.
• 45:51
  To benefit the greater good.
• 45:52
  I love that.
• 45:53
  That reminds me of a lot of those genealogy companies, right?
• 45:58
  They combine birth records, death records, loads of things.
• 46:03
  Anybody that anybody's ever put in becomes a part of the puzzle that just gets more useful as the data sets build.
• 46:11
  So you've convinced me in a way that actually giving away all that stuff can have a greater benefit.
• 46:20
  And I can also benefit from everybody else doing it.
• 46:22
  You know, it's like donating blood.
• 46:24
  It's a benefit to somebody who I don't even know.
• 46:27
  And obviously sharing that information and the privacy around it.
• 46:30
  I'm a little bit more comfortable with that idea now.
• 46:32
  I just read a lot online about it.
• 46:34
  I think it's just because people don't know.
• 46:35
  And going back to your point about men and mental health and physical health.
• 46:39
  Right.
• 46:40
  When you and I last spoke, we talked about the idea and there have been various articles written about this.
• 46:45
  How people are becoming attached to AI as an alternative to a real relationship.
• 46:52
  And from your point of view, with the preventative health side, are you also focusing Very much on that men's mental health problem as well as the physical.
• 47:03
  We prevent what you're describing three ways.
• 47:09
  One, we limit how much time you can interact with the AI on a given day.
• 47:15
  The limit is 40 minutes.
• 47:17
  That is roughly the time that a typical therapist will spend with a patient.
• 47:21
  Most therapists in fact will not see you every day.
• 47:24
  They will see you a few times a week.
• 47:25
  We allow seven days a week up to 35, 40 minutes of interaction with the AI model.
• 47:31
  One of the problems that happens with therapists is, is people become emotionally attached to their therapist, sometimes with an erotic aspect.
• 47:40
  We trained our model to prevent what's called erotic transference.
• 47:46
  We also trained our model to avoid bias because there are well known instances of bias in society.
• 47:55
  And our model is trained to not act in a biased way in situations that might otherwise lend themselves to bias.
• 48:06
  Okay, you and I discussed, I won't go into details for intellectual property reasons, but our models are more advanced than anything in the world because every model out there is intuitive.
• 48:21
  We are counterintuitive and I won't explain what that means, but the other day there was an article that I believe it was chatgpt solved a 80 year old math problem called the Erdos problem and it did that with
• 48:40
  a counterintuitive solution that no human would ever pursue because it was counterintuitive.
• 48:47
  And you have to keep going on that solution knowing that it's counterintuitive and humans gave up.
• 48:54
  I built based on studying diagnosis and misdiagnosed and realized that the counterintuitive way was the more perfect way to diagnose than the intuitive way.
• 49:04
  Because it's looking at rather than what do we think this is?
• 49:09
  What do we think it might be, might not be everything.
• 49:14
  I.
• 49:14
  Don't want to go into specific.
• 49:15
  I mean I do have a patent issue but I still don't want to go.
• 49:18
  Yeah, yeah, I'm not, I was just talking about the idea of counterintuitive which, which actually founders listening.
• 49:25
  Don't assume the easiest answer is also the right answer.
• 49:29
  Look at various data sets, look at various ways of approaching a problem.
• 49:33
  I think there's.
• 49:34
  Without going into the pattern, there's a great lesson there which is don't do the obvious thing when it comes to your business and trying to grow your startup.
• 49:45
  Look at many different avenues.
• 49:47
  Is that a fair tangent?
• 49:48
  Yeah, look, another math problem was partially solved.
• 49:52
  It's a hundred plus year old problem and it was solved by encouraging the model to keep going.
• 50:00
  And I have found the same thing.
• 50:01
  I sometimes have to encourage the model 60 plus times to keep going to give me the answer I want.
• 50:09
  After 60 plus times, I know when it's starting to give me dribble.
• 50:14
  These models, when they tell you they've given you the exhaustive answer.
• 50:17
  Never have.
• 50:18
  When we talked last time, I said it would be quite useful certainly for founders to understand how with this journey, this $4 million journey so far, getting all these patents, you mentioned well over 6 million.
• 50:30
  I put in 4, I raised 2 million from other 2 million plus.
• 50:34
  Okay, so 6 in total.
• 50:35
  Even more compelling.
• 50:36
  You and I had this discussion and you said I fumbled it at one point.
• 50:41
  What did you mean and what did you learn from?
• 50:44
  Because you've been involved in so many startups, right.
• 50:46
  I'm sure that there is a sort of using your red flash and light as nine year old analogy.
• 50:52
  Is there something that you saw later on that was a consistent theme?
• 50:58
  Yeah, I solved a very significant functional problem.
• 51:04
  Okay.
• 51:06
  That is not necessarily commercializable by me.
• 51:10
  I found that just because I built an AI model better than a doctor, it still can't prescribe because doctors legally have that ability.
• 51:22
  And doctors weren't going to use it even though it was in the best interest of their patients because they couldn't change their habits and they couldn't put aside their egos and their job descriptions for something superior.
• 51:37
  Not one doctor has ever said to me, I'm not going to use your product because I'm smarter than it.
• 51:45
  Not one doctor has ever said that.
• 51:48
  No doctor has ever said because I can get a better answer than your product.
• 51:52
  Never said that.
• 51:54
  Not one.
• 51:55
  It just, I don't want to change the way I practice.
• 51:58
  That's what they say,.
• 51:58
  Change.
• 51:59
  So you're saying the biggest friction in many instances from what I hear you saying, is if people are resistant to change.
• 52:07
  And liability, okay, they're afraid because we collect more information than they do, that if they have to review it, which they feel compelled to do, which is not a bad idea to review it, there might be something important
• 52:20
  that they miss and they will be held liable.
• 52:22
  So they prefer, prefer not to know.
• 52:25
  And this is not something said to us once in a while.
• 52:29
  It's like almost the chorus to the song.
• 52:33
  We hear it all the time.
• 52:34
  Don't give me more information on my patients.
• 52:36
  I don't have enough time for the information I already have.
• 52:38
  And then I'm going to miss something.
• 52:40
  Yeah, I still only got 20 minutes or 10 minutes.
• 52:44
  Obviously you're driven by this mission because your brother's Passing.
• 52:48
  Did this mission ever stop you behaving rationally as a business person?
• 52:52
  What would you redo if you could redo?
• 52:55
  And what has been the most expensive lesson so far?
• 52:58
  Not try to build something for doctors at this point.
• 53:02
  Okay, so that was the first mistake.
• 53:06
  The second mistake is I would not have tried to work with, which I believe is monopolistic, which I believe operates in the worst interests of American consumers, which is nestled with too much power, which is stopping, in my
• 53:25
  opinion, significant innovation in healthcare.
• 53:29
  But its owner is a multi billionaire and it has sold multibillion dollar packages to health systems.
• 53:39
  So they're at its mercy.
• 53:42
  And that is significantly, in my opinion, constraining healthcare changes.
• 53:48
  So I dealt with them and very unfortunate experiences.
• 53:53
  One of those experience included.
• 53:56
  We had a contract to do a study to demonstrate the value of our product.
• 54:01
  We believe.
• 54:02
  I can't prove this for fact.
• 54:03
  Well, I'll tell you the reason I believe that is.
• 54:06
  Two days after they canceled our contract, they signed a deal.
• 54:10
  And by canceling my contract, they did the worst thing possible.
• 54:15
  They paid me.
• 54:18
  They actually.
• 54:19
  They paid you off to cancel the contract early.
• 54:21
  And wouldn't let me do the work.
• 54:23
  It's the worst thing possible because if you say they didn't have the money, okay, well, hard to believe them because they know they had the money.
• 54:30
  You would say they didn't see the quality in the work.
• 54:34
  They didn't value it.
• 54:35
  They had some reason for canceling it.
• 54:38
  But when somebody says I'm paying you in full, I'm just not.
• 54:42
  Even though you're not doing it.
• 54:44
  Yeah,.
• 54:44
  Yeah,.
• 54:46
  That to me, I was happy to get the money, but from my perspective, that was the most unethical thing they could do, particularly since the contract was written by them and it was not terminable unless both sides agreed
• 55:01
  and their perspective was, we're paying you in full, we can do whatever we want.
• 55:06
  And my perspective was you had an agreement with me to do this study, let me finish the study, and when I say finish, all the prep work was done.
• 55:16
  We never actually got into the clinic.
• 55:18
  There was nothing wrong with the prep work.
• 55:21
  If there was, they wouldn't have paid us in full.
• 55:24
  Stop and think about that.
• 55:26
  I definitely know that without question.
• 55:28
  Right.
• 55:29
  I don't know enough about whole timeline there, but I do know that there are often, especially when it comes to healthcare and pharmaceuticals and everything, there are always lots of competing interests with a hell of a lot of
• 55:41
  money.
• 55:41
  Right.
• 55:42
  Around.
• 55:43
  Nothing like healthcare can.
• 55:45
  Stifle innovation.
• 55:46
  Really unfortunate.
• 55:47
  The good news is you got that money, it helped fund your development.
• 55:52
  So there's a silver lining in a way.
• 55:56
  To make changes to our application, which we did without charging them more money and they never refunded us for those costs.
• 56:05
  So it was insult to injury.
• 56:08
  But don't give up, people.
• 56:10
  That is a hell of a situation to find yourself in.
• 56:14
  Yeah, I don't want.
• 56:15
  Look, I don't.
• 56:15
  I'm not here to bad mouth the.
• 56:17
  No, no, no, no, I'm not.
• 56:18
  Me neither.
• 56:19
  Yes, but their administration is among the worst and I've heard that from many people.
• 56:24
  Thank you so much, Steven, for everything so far.
• 56:26
  And listeners, if you have a question for Steven, please pop them below in the comments.
• 56:29
  I'll make sure he sees them.
• 56:31
  Just to kind of round this one off, you've also experienced ageism from investors because unless you want to reveal your age, certainly you look very good for your age.
• 56:43
  I'm 67 years old and I've been told you know too much, you've learned too much, you have too many ideas.
• 56:50
  We can't invest in someone like you because you could keel over.
• 56:55
  Okay, well, I have a whole team around me.
• 56:59
  Maybe it's just a BS excuse, but we're not a society that appreciates older people.
• 57:05
  Investors are all about de risking now.
• 57:09
  And age is a risk factor.
• 57:11
  Even though studies show 60 year olds start the most successful company.
• 57:14
  I do not know that.
• 57:16
  I.
• 57:16
  I did know that most millionaires, contrary to popular belief, first of all, are not the first idea that somebody's come up with.
• 57:23
  It's usually 3rd, 4th, 5th, 6th, 7th, and also it's usually 1st million after 40.
• 57:29
  Age and experience are very important.
• 57:32
  Okay, look, thank God I've done very well in life that I even had the money to spend 4 million-plus on a mission that I found very important, near and dear to me.
• 57:48
  Not everybody has that.
• 57:49
  Okay,.
• 57:50
  Yeah.
• 57:51
  So it's hard to put yourself in the same situation.
• 57:54
  But trust me when I tell you, nothing has ever come easy to me.
• 58:00
  Nothing.
• 58:02
  From the earliest age, nothing has come easy.
• 58:06
  I overheard my second grade teacher tell my parents that I was fundamentally lazy.
• 58:13
  My whole life I thought I was the hardest working, fundamentally lazy person on the planet.
• 58:19
  There you go.
• 58:19
  There's the title for the episode.
• 58:21
  I'm kidding.
• 58:22
  Hardest working, fundamentally lazy.
• 58:24
  Like, isn't it weird how whatever you're.
• 58:27
  And I think this is a really important point to put a serious note on it.
• 58:30
  We all carry whatever that version of the second grade teacher is, that thing that we remember with us and you know, you're another Example of proving that it's just not true.
• 58:42
  I carried that actually for many years, but it was replaced by something my mother said and my brother said.
• 58:49
  My mother said, work hard.
• 58:51
  So you're in a position to choose.
• 58:53
  Freedom.
• 58:53
  Yeah, real freedom.
• 58:55
  My middle brother said to me, you should know it's possible to succeed at anything you do.
• 59:02
  And my older brother said to me, with your attitude, I don't see you succeeding at anything you do because failure is easy.
• 59:10
  And those were two pillars, which is failure is easy, but success is possible.
• 59:14
  I like that.
• 59:15
  Failure is easy, but success is possible.
• 59:18
  I always tend to, whenever I talk to founders myself, in whatever capacity, I always talk about the idea that you never know when the next yes is going to come or the yes or whatever outcome.
• 59:29
  You know, just by sheer great determination and keeping in the game, you stand, obviously, a much higher chance of success.
• 59:39
  I like the idea of failing's easy and success is possible.
• 59:44
  I thank you so much for all of this.
• 59:47
  You know, just one other point on the age thing.
• 59:50
  You're a doctor, a Harvard mba, a Stanford fellow, and you've already built and exited a major company, yet you've encountered that resistance from investors because of your age.
• 1:00:01
  I mean, it wasn't the only thing I've heard.
• 1:00:02
  I also heard, come back when you have significant traction.
• 1:00:05
  That was the most common refrain.
• 1:00:07
  That is a very common one, isn't it?
• 1:00:09
  Yeah.
• 1:00:09
  Yeah.
• 1:00:10
  Look, nobody cares about what you've done in the past.
• 1:00:13
  It's, what are you doing right now?
• 1:00:15
  What can you do for me?
• 1:00:16
  And.
• 1:00:17
  Never rest on your laurels.
• 1:00:18
  You can't.
• 1:00:19
  What have you done for me lately?
• 1:00:21
  That's all that people care about.
• 1:00:22
  What can you do for me now?
• 1:00:23
  That's what people care about.
• 1:00:25
  Does venture capital romanticize young founders and the whole thing.
• 1:00:31
  Romantic.
• 1:00:31
  Romanticize that idea.
• 1:00:33
  And what do you think you see at 67 that a 37, 30 or 37 year old might miss?
• 1:00:40
  I don't think they romanticize young people.
• 1:00:42
  They randomize young people graduating from the top schools, okay, who've already proven themselves to be exceptional in one way or another.
• 1:00:49
  I always tell this story when I went to visit my first investor, which was based in Ireland.
• 1:00:55
  I got the Wall Street Journal read on the flight, and I cut out an article which I brought to the meeting.
• 1:01:02
  It said young entrepreneurs have an advantage over older entrepreneurs.
• 1:01:07
  They don't know where all the minds are planted, and so they go bravely.
• 1:01:15
  Naivety is an advantage in a way, is what I think you're saying.
• 1:01:18
  In other words, fear is A disadvantage.
• 1:01:20
  I know all the things that can go wrong and they hold you back.
• 1:01:25
  The young person goes bravely.
• 1:01:27
  Now, most people fail in startups.
• 1:01:29
  That's just the reality of the situation.
• 1:01:31
  But those that succeed oftentimes were just lucky.
• 1:01:34
  They stepped over the minefields.
• 1:01:37
  Stepped over, stepped over, stepped over.
• 1:01:39
  Happenstance to that point, you've kind of gotten over all the landmines,.
• 1:01:44
  So to speak.
• 1:01:45
  You've had the exit that most people would dream of.
• 1:01:47
  Did it actually feel like winning?
• 1:01:50
  You know, people always ask, was there?
• 1:01:52
  Do you remember those giant checks they used to deliver with the lottery?
• 1:01:55
  Was there like a moment or a thing that happened?
• 1:01:58
  And what did it mean?
• 1:01:59
  Did it mean everything you wanted it to mean?
• 1:02:01
  That's a personality question.
• 1:02:03
  And my personality is.
• 1:02:06
  We went out with some friends to celebrate, but I wasn't done right after I sold it.
• 1:02:13
  That year was the worst year of my life.
• 1:02:15
  I was depressed, okay?
• 1:02:18
  I went from answering thousands of questions every day to nobody reaching out.
• 1:02:22
  My friends were all still working.
• 1:02:24
  My wife was busy with her friends.
• 1:02:26
  I played the stock market that year with my brother, one of my older, my oldest brother.
• 1:02:31
  And we made a lot of money.
• 1:02:33
  We made over 700,000 that year playing the stock market.
• 1:02:37
  Why?
• 1:02:38
  Because every bet paid off.
• 1:02:39
  It was just that kind of yield.
• 1:02:41
  Things went down, but they all came back.
• 1:02:43
  But I hated it.
• 1:02:45
  Hated it because it was like gambling, right?
• 1:02:47
  I was gambling and I was winning.
• 1:02:49
  But it wasn't intellectually satisfying.
• 1:02:50
  It wasn't meaningful.
• 1:02:52
  I wasn't doing well by doing good.
• 1:02:55
  I hated it.
• 1:02:57
  That was 17, 18 years ago.
• 1:02:59
  I haven't had a paycheck since.
• 1:03:00
  My point is, we're all different people.
• 1:03:03
  And when it came to me, I feel the need to do something meaningful.
• 1:03:09
  It's fundamental to my psychological well being, to achieve things and to do well by doing good.
• 1:03:18
  Just to sort of wrap it up.
• 1:03:20
  If Madhum saves a million lives but never becomes a billion dollar ipo, you know, whatever, but you improve.
• 1:03:28
  I will be ecstatic.
• 1:03:30
  I will be ecstatic.
• 1:03:31
  I don't need a billion dollars.
• 1:03:33
  I see most billionaires, they don't stay married, okay?
• 1:03:37
  And they're probably miserable.
• 1:03:39
  I've been married 42 years, okay?
• 1:03:42
  Still love the woman that I married two and a half months after I met her.
• 1:03:48
  Money corrupts.
• 1:03:49
  Too much money corrupts, okay?
• 1:03:51
  I have enough to be happy.
• 1:03:52
  That's it.
• 1:03:53
  I'm not chasing money.
• 1:03:54
  Money is not something you chase.
• 1:03:57
  You know why?
• 1:03:57
  You can't take it with you.
• 1:03:59
  You could leave it behind.
• 1:04:00
  And I've been Very generous with my kids already.
• 1:04:03
  So they're all set.
• 1:04:05
  And there's an amazing freedom that comes with.
• 1:04:08
  You don't need a monetary payoff.
• 1:04:10
  Like, you're not chasing a figure, you're chasing a mission.
• 1:04:15
  And so if I may ask one last question, when this chapter eventually, and you've built this and whatever that becomes, what needs to be true for you to say, I did what I came here to do.
• 1:04:31
  In a trackable way.
• 1:04:33
  I impacted a million lives, saved them from death or disability, and I returned a fair return to my investors.
• 1:04:44
  So everybody wins.
• 1:04:45
  Everybody wins,.
• 1:04:46
  People.
• 1:04:47
  Yeah.
• 1:04:47
  I love it.
• 1:04:48
  I love it.
• 1:04:49
  I just want to say thank you so much for your time.
• 1:04:51
  We've gone a bit over, but I knew you'd be a great person to hear from.
• 1:04:56
  If anybody has any questions, I will pop details to Steven and to his company so you can go and check it out.
• 1:05:03
  Whether you're an investor, a founder, or a member of the general public who's really interested in what he's driven by and what he's doing motivated by his brother's death.
• 1:05:12
  My phone number, website.
• 1:05:14
  Okay.
• 1:05:15
  All I ask is if you text me, tell me who you are and why you're texting me, it's on the website.
• 1:05:19
  My personal, one and only phone number is on the website.
• 1:05:23
  Okay, I will also, for those of you who want right now, right here, I'll pop it down below and I'll pop all the details to Stephen and his company as well.
• 1:05:32
  Thank you so much, Steven, for your time.
• 1:05:34
  I really appreciate it.
• 1:05:36
  And again, folks, if you have any comments, any questions for Stephen, pop them down below in the comments.
• 1:05:41
  I'll make sure Stephen sees them.
• 1:05:42
  Thank you very much, Dr.
• 1:05:43
  Stephen Charlip, for your time today on the i1 podcast.
• 1:05:47
  And have a great day, everybody.